An insulation performance evaluation system and method for an insulation voltage transformer

Abstract

The present application relates to the technical field of electric energy metering, and particularly relates to an insulation performance evaluation system and method for insulation voltage transformers, comprising an insulation monitoring and sensing optimization module, a central storage unit, a data processing and abnormality analysis module, and a device performance evaluation module; the insulation monitoring and sensing optimization module builds an intelligent sensor network and a data acquisition and processing terminal, and realizes real-time monitoring of the insulation voltage transformer; the data processing and abnormality analysis module extracts key features, and uses a local outlier factor algorithm to perform real-time analysis on the key features, and can timely discover abnormal changes in insulation performance; the device performance evaluation module calculates a comprehensive gain value index to perform detailed performance evaluation on the device with problems, which is helpful for operation and maintenance personnel to understand the device state and formulate reasonable maintenance, replacement or optimization strategies. The present application is used to solve the technical problems of incomplete insulation performance monitoring and insufficient evaluation for insulation voltage transformers.

Description

Technical Field

[0001] This invention relates to the field of electrical energy metering technology, and in particular to a system and method for evaluating the insulation performance of an insulation voltage transformer. Background Technology

[0002] Electricity metering technology refers to the technical means of measuring, calculating and recording electrical energy to monitor, evaluate and manage electricity use. By accurately and reliably measuring the consumption and supply of electrical energy, it provides important data support for the operation and management of power systems.

[0003] With the continuous development of technology, electricity metering technology will also continue to innovate and progress. Introducing this technology into the insulation performance evaluation of voltage transformers can solve the following problems: Insulation status is difficult to accurately assess; traditional evaluation methods may not be able to comprehensively and accurately reflect the insulation status of voltage transformers, leading to the failure to detect potential insulation defects in a timely manner. Incomplete insulation monitoring and poor real-time performance; traditional insulation voltage transformer monitoring methods may have problems with incomplete monitoring parameters and insufficient real-time monitoring, resulting in the inability to detect subtle changes in insulation performance or sudden faults in a timely manner, thus affecting the stable operation of the power system. Inappropriate sensor selection and configuration; how to rationally select and optimize the configuration of sensors based on the characteristics and monitoring needs of different insulation voltage transformers is a current technical challenge. Low levels of intelligence and automation; existing insulation monitoring and evaluation systems may lack intelligent and automated functions, failing to achieve automated monitoring and evaluation of insulation voltage transformers, increasing manual intervention and maintenance costs. Insufficient data processing and anomaly analysis capabilities; the massive amounts of data generated by insulation monitoring need to be processed and analyzed efficiently and accurately; however, existing data processing and anomaly analysis technologies may not meet this need, leading to false alarms or missed alarms for insulation faults. Therefore, this invention proposes a system and method for evaluating the insulation performance of an insulated voltage transformer. Summary of the Invention

[0004] The purpose of this invention is to solve the problems in the background art by proposing an insulation performance evaluation system and method for insulation voltage transformers.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An insulation performance evaluation system for an insulation voltage transformer includes: an insulation monitoring and sensing optimization module, a central storage unit, a data processing and anomaly analysis module, and an equipment performance evaluation module;

[0007] The insulation monitoring and sensing optimization module is used to determine the monitoring parameters of the insulation voltage transformer, build an intelligent sensor network, and configure a data acquisition and processing terminal to collect data; the raw data collected by the module is transmitted to the central storage unit in real time via wireless means.

[0008] The data processing and anomaly analysis module is used to extract key features of insulation performance from the raw data stored in the central storage unit, and to use the local anomaly factor algorithm to analyze the anomaly patterns in the key features in real time to identify possible precursors of insulation faults.

[0009] The central storage unit is the central link in the data flow, responsible for receiving and storing the raw data transmitted wirelessly from the insulation monitoring and sensing optimization module; enabling the data processing and anomaly analysis module to access the necessary data resources for further analysis and processing.

[0010] The equipment performance evaluation module is used to filter and identify insulated voltage transformers that are faulty or in abnormal condition. It performs performance evaluation on insulated voltage transformers that are faulty or in abnormal condition by calculating the comprehensive gain value in order to implement maintenance plans. Here, insulated voltage transformers that are faulty or in abnormal condition have the same meaning as faulty or abnormal equipment.

[0011] It should be noted that the insulation performance evaluation system and method for insulation voltage transformers in this embodiment of the invention can be widely applied to equipment status management or operation and maintenance control in power systems. It can be used to monitor the insulation performance, temperature changes, electrical parameters, and environmental conditions of insulation voltage transformers during operation. Specifically, it can perform real-time monitoring of insulation voltage transformers using advanced sensing and data processing technologies. This invention, through multiple stages including sensor optimization and data acquisition in the insulation monitoring and sensing optimization module, data analysis and anomaly identification in the data processing and anomaly analysis module, and performance evaluation and maintenance plan formulation in the equipment performance evaluation module, achieves a highly efficient and intelligent insulation performance evaluation system for insulation voltage transformers. This system is of great significance for improving the automation level of power systems, reducing operation and maintenance costs, and extending equipment lifespan.

[0012] Furthermore, the insulation monitoring and sensing optimization module clarifies the monitoring parameters of the insulation voltage transformer, establishes an intelligent sensor network, and configures a data acquisition and processing terminal for data acquisition. The process includes:

[0013] Based on the model, specifications, and operating environment of the insulation voltage transformer, the specific parameters of the monitoring target are determined, including voltage, current, temperature, humidity, and gas concentration; among them, the monitoring target is the insulation voltage transformer, and the specific parameters are the key factors affecting the insulation performance.

[0014] Select sensors based on the monitoring targets and specific parameters to ensure comprehensive coverage of the areas requiring monitoring.

[0015] Understandably, when selecting a sensor, factors such as the sensor's installation location, installation method, measurement range, accuracy, stability, and compatibility with other devices must also be considered to ensure that the sensor can accurately and reliably measure the required physical quantities and transmit the data to the data acquisition and processing terminal for further processing and analysis.

[0016] After selecting the sensors, the minimum vertex coverage method is used to optimize the sensor layout, that is, the arrangement of monitoring points, to ensure that all key monitoring points are covered while minimizing the number of sensors.

[0017] The shortest path algorithm is used for the communication design of the sensor network to ensure that all sensor data can be transmitted to the central storage unit efficiently and reliably.

[0018] Based on the determined monitoring points and sensor selection results, install sensors around the voltage transformer to ensure good contact or appropriate distance between the sensors and the monitored object in order to accurately obtain monitoring data.

[0019] Configure the data acquisition and processing terminal to collect data, set the data acquisition frequency and accuracy range to ensure the real-time performance and accuracy of the data for subsequent data analysis and processing.

[0020] Furthermore, the process of optimizing the sensor layout using the minimum vertex coverage method includes:

[0021] Define the sensor's coverage area as Treat all monitoring points as a map The vertices in; where, It represents the set consisting of all monitoring points. Represents the possible set of connection edges;

[0022] For each monitoring point Define an indicator variable ;when When, it means at Position sensor, when When the value is set to 0, it indicates that no sensor is placed; where, Indicates an index;

[0023] For each monitoring point There is at least one monitoring point. cover That is, it exists Make:

[0024] In the formula, This is an element identifier used to indicate the overlay status. Indicates an index; where, express exist Within its coverage area, This indicates that the area is not covered.

[0025] Minimize the total number of sensors required, i.e. ;

[0026] This involves transforming the sensor placement problem into finding a graph. A set of vertices , making Radius of any vertex All vertices within, simultaneously The goal is to find the minimum value of , that is, to find a minimum subset of vertices such that Each vertex in the data must cover at least one monitoring point;

[0027] Understandably, the layout planning of sensor nodes is carried out in combination with the structural and insulation performance requirements of voltage transformers to obtain all possible monitoring points in the network. This includes determining the key locations that need to be monitored and considering the installation location, angle, orientation, and relative position of the sensors with other devices when arranging the monitoring points to ensure the accuracy and reliability of the monitoring data.

[0028] Furthermore, the process of designing communication for sensor networks using shortest path algorithms includes:

[0029] Modeling sensor networks as weighted graphs ;in, Represents a set of sensor nodes. This represents the set of communication paths connecting sensor nodes, where each path is a separate path. Assign a weight This represents communication delay or energy consumption;

[0030] Understandably, based on the actual layout of the voltage transformer, monitoring requirements, and communication methods between sensors, the topology of the sensor network and the data transmission method are designed. When designing the topology diagram, it is necessary to clarify the location, number, communication path, and connection relationship with other nodes of each sensor node to ensure that the data can be transmitted efficiently and accurately.

[0031] Get source node and target node ,from arrive Among all possible paths, select the path with the minimum total weight; let the source node... To the target node The distance is 0, source node The distance to all other nodes is infinite;

[0032] The shortest path algorithm starts by setting the source node... Add to the deterministic set; subsequently, each time, select the node furthest from the undeterministic set. The most recent node is added to the deterministic set, and all its neighbors are updated to the source node. The distance (if this node allows access from a neighbor to the source node) (If the distance is shorter), repeat this process until all nodes are added to the set, that is, the shortest distance of all nodes is determined.

[0033] Furthermore, the process of configuring the data acquisition and processing terminal to collect data, and setting the data acquisition frequency and accuracy range, includes:

[0034] Determine the data acquisition frequency and accuracy range;

[0035] Understandably, the required data acquisition frequency and accuracy range are determined based on the characteristics of the insulation voltage transformer, the testing requirements, and the expected data analysis accuracy.

[0036] Select and configure the data acquisition and processing terminal. Use a data acquisition and processing terminal with appropriate processing capabilities and accuracy, and ensure that the terminal can support the set data acquisition frequency and has sufficient resolution to meet the accuracy requirements.

[0037] The sampling frequency value is obtained through the following formula: Sampling frequency value = Number of data points required / Test cycle; where the sampling frequency value represents the number of data points collected per second, in Hz, and the test cycle can be converted to seconds according to the actual situation; based on the test cycle (such as one day, one week, etc.) and the total number of data points required, the appropriate sampling frequency value can be deduced.

[0038] Determining the accuracy range using ADC resolution: Assuming the ADC resolution is... Then the accuracy At least satisfy Accuracy is typically measured by quantization error or resolution. Determine the required level of accuracy, such as the minimum resolvable value for voltage measurements. Check the analog-to-digital converter (ADC) specifications of the data acquisition and processing terminal to ensure that its resolution meets or exceeds the required accuracy requirements. In the terminal configuration, select or set the appropriate ADC resolution and gain to ensure the accuracy of data acquisition.

[0039] Understandably, after configuration, preliminary data acquisition tests should be conducted to verify whether the actual acquisition frequency and accuracy meet the set requirements. If there are deviations, the configuration parameters can be adjusted according to the test results until the requirements are met.

[0040] Furthermore, the data processing and anomaly analysis module extracts key features of insulation performance from the raw data stored in the central storage unit, and uses the local anomaly factor algorithm to analyze anomaly patterns in the key features in real time. This process includes:

[0041] By constructing a sensor wireless communication network, the data stream of each sensor is acquired:

[0042] In the formula, Indicates sensor In time Data flow; Indicates sensor No. Such measurement in time The reading; Indicates the dimension of observation;

[0043] Integrate all sensor data streams to obtain the sensor monitoring dataset, i.e. ;in, This represents the total number of sensors, and ;

[0044] Sensor monitoring dataset Preprocessing is performed to identify indicators related to insulation performance, and a preprocessed monitoring dataset is obtained. Remove invalid or outlier measurements; use a sliding window method to detect and eliminate outliers. ,make In time arrive The average value within is The standard deviation is Then the outlier is defined as Outliers were removed, the preprocessed monitoring dataset was compiled, and labeled as... ;

[0045] Based on the preprocessed monitoring dataset The key features of insulation performance are extracted to obtain the first key feature, the second key feature and the third key feature;

[0046] By combining the first key feature, the second key feature, and the third key feature, a feature set is obtained. ,in, Indicates the number of features extracted;

[0047] Using local anomaly factors Real-time analysis of anomalous patterns in key features:

[0048] H1. Calculate the local density deviation of each sample feature relative to its neighbors, let... Any sample feature in ,That The value is defined as follows: In the formula, express The set of nearest neighbors; Indicates density;

[0049] H2. The calculated value of the local density deviation is used as the anomaly score.

[0050] H3. Set an anomaly threshold, compare the anomaly score with the anomaly threshold, and if the anomaly score is less than the anomaly threshold, determine the sample characteristics. The score is considered normal; if the abnormal score is not less than the abnormal threshold, then the sample characteristics are determined. These are potential anomalies and are marked as anomalous features;

[0051] Understandably, for features marked as anomalous, in-depth analysis is performed to identify potential precursors to insulation failure.

[0052] Furthermore, based on the preprocessed monitoring dataset, the process of extracting key features of insulation performance to obtain the first, second, and third key features includes:

[0053] The harmonic component characteristics were extracted by analyzing the current and voltage signals using Fourier transform and marked as the first key feature: In the formula, This represents the signal in the frequency domain. Represents the original signal; This represents the relationship between frequency and time, specifically rotation and oscillation in the complex plane. The imaginary unit is used to ensure the direction of rotation of vectors in the complex plane. Represents frequency variables. This determines the rotational speed, i.e., the frequency. Integral operations, the expression for... The integral element;

[0054] The temperature gradient feature is obtained by calculating the ratio of the temperature difference to the distance between two points, and is marked as the second key feature: In the formula, This represents the temperature values ​​at two points. Indicates the distance between two points;

[0055] By calculating the pulse amplitude and repetition rate of the discharge, partial discharge characteristics are obtained and marked as the third key feature: In the formula, Indicates the pulse amplitude; They represent time. The maximum and minimum voltage values; Indicates the discharge repetition rate; Indicates the number of discharges; Indicates a unit of time.

[0056] Furthermore, the equipment performance evaluation module filters and identifies insulation voltage transformers with faults or abnormal conditions. The process of evaluating the performance of insulation voltage transformers with faults or abnormal conditions by calculating the comprehensive gain value includes:

[0057] Based on the abnormal feature values ​​output by the data processing and anomaly analysis module, the insulation voltage transformers with faults or abnormal states are identified and marked.

[0058] For equipment that is identified as faulty or in an abnormal state, extract its historical operating data and maintenance records from the database;

[0059] Understandably, this data includes, but is not limited to: historical operating data: voltage fluctuations, current changes, temperature profiles, humidity records, gas concentration monitoring, etc., which reflect the operating status and environmental conditions of the equipment at different points in time; maintenance records: the time, content, replaced parts, and post-maintenance test results of each maintenance, which are crucial for analyzing the causes and speed of equipment performance degradation.

[0060] After obtaining historical operating data and maintenance records of faulty or abnormal equipment, the performance analysis phase begins:

[0061] The overall performance of faulty or abnormal equipment is evaluated by the comprehensive gain value. The formula for calculating the comprehensive gain is as follows: In the formula, The overall gain value is a quantitative parameter that integrates multiple performance indicators and is used to reflect the overall performance of the equipment under different conditions. Indicates the stability gain value; Indicates the efficiency gain value; Indicates the reliability gain value; These represent the weights of the stability gain, efficiency gain, and reliability gain, respectively. The weighting depends on the importance of each performance indicator to the overall performance evaluation;

[0062] Understandably, stability gain can be calculated based on the fluctuation of key parameters of the equipment; the better the stability, the higher the gain value. Efficiency gain reflects the efficiency of the equipment in the energy conversion or transmission process. For insulated voltage transformers, it may involve the accuracy and efficiency of voltage conversion. Reliability gain is calculated based on the failure rate and maintenance cycle of the equipment; the lower the failure rate and the longer the maintenance cycle, the higher the reliability gain.

[0063] When classifying faulty or abnormal equipment based on the overall gain value, the overall gain value of the faulty or abnormal equipment is matched with a preset gain threshold to obtain a normal maintenance label, an important maintenance label, or an emergency maintenance label.

[0064] Specifically, if the overall gain value is less than a preset gain threshold, the device in a faulty or abnormal state is judged to have extremely poor performance, a first maintenance signal is generated, and the corresponding faulty or abnormal state device is associated with an emergency maintenance tag; if the overall gain value is not less than a preset gain threshold and not greater than a preset gain threshold, ... , If the value is a real number greater than one hundred, the device is judged to have poor performance due to a fault or abnormal state, a second maintenance signal is generated, and the corresponding fault or abnormal state device is associated with an important maintenance tag; if the overall gain value is greater than a preset gain threshold... If the device is found to be in a faulty or abnormal state, its performance is deemed to be generally poor, a third maintenance signal is generated, and the corresponding faulty or abnormal state device is associated with a normal maintenance tag.

[0065] Based on the results of the comprehensive gain value assessment, an equipment maintenance plan is formulated: For the first maintenance signal, the equipment performance is severely degraded and cannot be restored through maintenance, requiring upgrades or replacement to improve the reliability and efficiency of the entire system; for the second maintenance signal, the equipment has already failed and requires corrective maintenance to ensure that the equipment returns to normal operation; for the third maintenance signal, the equipment performance has declined but has not yet failed, requiring preventive maintenance, such as replacing aging parts and adjusting operating parameters, to prevent failures from occurring.

[0066] A method for evaluating the insulation performance of an insulated voltage transformer includes:

[0067] Define the monitoring parameters of the insulation voltage transformer, build an intelligent sensor network, and configure a data acquisition and processing terminal to collect data; transmit the collected raw data to the central storage unit in real time via wireless means.

[0068] Key features of insulation performance are extracted from the raw data stored in the central storage unit, and the local anomaly factor algorithm is used to analyze the anomaly patterns in the key features in real time.

[0069] The system filters and identifies insulated voltage transformers that are faulty or in abnormal condition, and evaluates their performance by calculating the comprehensive gain value.

[0070] Compared with existing technologies, the advantages of this invention in providing an insulation performance evaluation system and method for insulation voltage transformers are as follows:

[0071] This invention clarifies the monitoring parameters of the insulation voltage transformer, selects sensors based on these parameters, builds and optimizes an intelligent sensor network, and configures a data acquisition and processing terminal. This avoids data redundancy and invalid monitoring, improving the targeting and effectiveness of monitoring. The intelligent characteristics of the sensor network also enable the system to automatically adapt to environmental changes, improving the stability and reliability of monitoring. The raw data collected by the module is transmitted to the central storage unit in real time via wireless means, effectively reducing data transmission delays and errors.

[0072] This invention extracts key features of insulation performance and uses a local anomaly factor algorithm to analyze abnormal patterns in these key features in real time, identifying possible precursors to insulation faults. This provides a valuable data foundation for subsequent analysis, reduces the complexity and workload of data analysis, and enables timely detection of subtle changes in insulation performance.

[0073] This invention improves the accuracy of fault identification by filtering and identifying insulated voltage transformers with faults or abnormal conditions. It also calculates gain values ​​to evaluate the performance of insulated voltage transformers with faults or abnormal conditions, so as to formulate corresponding equipment maintenance plans. This provides scientific basis and decision support for operation and maintenance personnel, making operation and maintenance work more scientific, reasonable and efficient.

[0074] In summary, this invention, based on actual conditions, improves the real-time performance and accuracy of insulation performance monitoring through comprehensive data processing and analysis, enhances the reliability and stability of the system, and automates the entire data monitoring and performance evaluation process. This reduces the frequency and intensity of manual inspections, lowers maintenance costs, and collectively improves the efficiency and accuracy of monitoring, analyzing, and evaluating the insulation performance of insulation voltage transformers, thereby ensuring the normal operation of a subsequent insulation performance evaluation system and method for insulation voltage transformers. Attached Figure Description

[0075] Figure 1 This is a block diagram of an insulation performance evaluation system for an insulated voltage transformer proposed in this invention.

[0076] Figure 2 This is a flowchart of a method for evaluating the insulation performance of an insulated voltage transformer proposed in this invention. Detailed Implementation

[0077] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] Reference Figure 1 An insulation performance evaluation system for an insulation voltage transformer, comprising an insulation monitoring and sensing optimization module, a central storage unit, a data processing and anomaly analysis module, and an equipment performance evaluation module;

[0079] The insulation monitoring and sensing optimization module is used to determine the monitoring parameters of the insulation voltage transformer, build an intelligent sensor network, and configure a data acquisition and processing terminal to collect data; the raw data collected by the module is transmitted to the central storage unit in real time via wireless means.

[0080] The data processing and anomaly analysis module is used to extract key features of insulation performance from the raw data stored in the central storage unit, and to use the local anomaly factor algorithm to analyze the anomaly patterns in the key features in real time to identify possible precursors of insulation faults.

[0081] The central storage unit is the central link in the data flow, responsible for receiving and storing the raw data transmitted wirelessly from the insulation monitoring and sensing optimization module; enabling the data processing and anomaly analysis module to access the necessary data resources for further analysis and processing.

[0082] The equipment performance evaluation module is used to filter and identify insulated voltage transformers that are faulty or in abnormal condition. It performs performance evaluation on insulated voltage transformers that are faulty or in abnormal condition by calculating the comprehensive gain value in order to implement maintenance plans. Here, insulated voltage transformers that are faulty or in abnormal condition have the same meaning as faulty or abnormal equipment.

[0083] It should be noted that the insulation performance evaluation system and method for insulation voltage transformers in this embodiment of the invention can be widely applied to equipment status management or operation and maintenance control in power systems. It can be used to monitor the insulation performance, temperature changes, electrical parameters, and environmental conditions of insulation voltage transformers during operation. Specifically, it can perform real-time monitoring of insulation voltage transformers using advanced sensing and data processing technologies. This invention, through multiple stages including sensor optimization and data acquisition in the insulation monitoring and sensing optimization module, data analysis and anomaly identification in the data processing and anomaly analysis module, and performance evaluation and maintenance plan formulation in the equipment performance evaluation module, achieves a highly efficient and intelligent insulation performance evaluation system for insulation voltage transformers. This system is of great significance for improving the automation level of power systems, reducing operation and maintenance costs, and extending equipment lifespan.

[0084] The insulation monitoring and sensing optimization module clarifies the monitoring parameters of the insulation voltage transformer, establishes an intelligent sensor network, and configures a data acquisition and processing terminal to collect data. The steps include:

[0085] Step 101: Based on the model, specifications, and operating environment of the insulation voltage transformer, determine the specific parameters of the monitoring target, including voltage, current, temperature, humidity, and gas concentration; where the monitoring target is the insulation voltage transformer, and the specific parameters are the key factors affecting insulation performance.

[0086] Step 102: Select sensors based on the monitoring targets and specific parameters to ensure comprehensive coverage of the areas requiring monitoring;

[0087] In step 102, when selecting a sensor, factors such as the sensor's installation location, installation method, measurement range, accuracy, stability, and compatibility with other devices must be considered to ensure that the sensor can accurately and reliably measure the required physical quantities and transmit the data to the data acquisition and processing terminal for further processing and analysis. Simultaneously, the control methods for distance or contact range may differ for different types of sensors. For example, the measurement range of a non-contact sensor can be controlled by adjusting its measurement distance and angle. During setup, it is necessary to ensure that the distance between the sensor and the object being measured is appropriate, avoiding excessive proximity that could lead to measurement errors or overheating damage, and also avoiding excessive distance that could cause signal attenuation or prevent measurement. Contact sensors require close contact with the object being measured to obtain accurate measurement data. During setup, it is necessary to ensure that the sensor's contact surface is in full contact with the surface of the object being measured, avoiding gaps or looseness that could lead to measurement errors.

[0088] Step 103: After selecting the sensors, optimize the sensor layout using the minimum vertex coverage method, i.e., the arrangement of monitoring points, to ensure that all key monitoring points are covered while minimizing the number of sensors.

[0089] Define the sensor's coverage area as Treat all monitoring points as a map The vertices in; where, It represents the set consisting of all monitoring points. Represents the possible set of connection edges;

[0090] For each monitoring point Define an indicator variable ;when When, it means at Position sensor, when When the value is set to 0, it indicates that no sensor is placed; where, Indicates an index;

[0091] For each monitoring point There is at least one monitoring point. cover That is, it exists Make:

[0092] In the formula, This is an element identifier used to indicate the overlay status. Indicates an index; where, express exist Within its coverage area, This indicates that the area is not covered.

[0093] Minimize the total number of sensors required, i.e. ;

[0094] This involves transforming the sensor placement problem into finding a graph. A set of vertices , making Radius of any vertex All vertices within, simultaneously The goal is to find the minimum value of , that is, to find a minimum subset of vertices such that Each vertex in the data must cover at least one monitoring point;

[0095] In step 103, the layout planning of sensor nodes is carried out in combination with the structural and insulation performance requirements of voltage transformers, and all possible monitoring points in the network are obtained. This includes determining the key locations that need to be monitored, and when arranging monitoring points, the installation location, angle, direction and relative position of the sensors with other devices are considered to ensure the accuracy and reliability of the monitoring data.

[0096] Step 104: Design the communication of the sensor network using the shortest path algorithm to ensure that all sensor data can be transmitted to the central storage unit efficiently and reliably.

[0097] Modeling sensor networks as weighted graphs ;in, Represents a set of sensor nodes. This represents the set of communication paths connecting sensor nodes, where each path is a separate path. Assign a weight This represents communication delay or energy consumption;

[0098] Understandably, based on the actual layout of the voltage transformer, monitoring requirements, and communication methods between sensors, the topology of the sensor network and the data transmission method are designed. When designing the topology diagram, it is necessary to clarify the location, number, communication path, and connection relationship with other nodes of each sensor node to ensure that the data can be transmitted efficiently and accurately.

[0099] Get source node and target node ,from arrive Among all possible paths, select the path with the minimum total weight; let the source node... To the target node The distance is 0, source node The distance to all other nodes is infinite;

[0100] The shortest path algorithm starts by setting the source node... Add to the deterministic set; subsequently, each time, select the node furthest from the undeterministic set. The most recent node is added to the deterministic set, and all its neighbors are updated to the source node. The distance (if this node allows access from a neighbor to the source node) (If the distance is shorter), repeat this process until all nodes are added to a definite set, that is, the shortest distance of all nodes is determined;

[0101] Step 105: Based on the determined monitoring points and sensor selection results, install sensors around the voltage transformer, ensuring good contact or an appropriate distance between the sensors and the monitored object to accurately obtain monitoring data.

[0102] Step 106: Configure the data acquisition and processing terminal to collect data, setting the data acquisition frequency and accuracy range to ensure the real-time performance and accuracy of the data for subsequent data analysis and processing.

[0103] Determining the data acquisition frequency and accuracy range can be understood as follows: the required data acquisition frequency and accuracy range are determined based on the characteristics of the insulation voltage transformer, the test requirements, and the expected data analysis accuracy. For example, if it is necessary to capture rapidly changing insulation performance parameters, a higher acquisition frequency may be required, while if the focus is on long-term trends, the acquisition frequency can be appropriately reduced.

[0104] Select and configure the data acquisition and processing terminal. Use a data acquisition and processing terminal with appropriate processing capabilities and accuracy, and ensure that the terminal can support the set data acquisition frequency and has sufficient resolution to meet the accuracy requirements.

[0105] The acquisition frequency value is obtained through the following formula: Acquisition frequency value = Number of data points required / Test cycle; where the acquisition frequency value represents the number of data points collected per second, in Hz, and the test cycle can be converted to seconds according to the actual situation; based on the test cycle (such as one day, one week, etc.) and the total number of data points required, the appropriate acquisition frequency value is deduced; for example, if 1000 data points need to be collected in one hour, then the acquisition frequency value = 1000 / 3600 ≈ 0.28Hz; the acquisition frequency value is entered and saved in the settings interface of the data acquisition and processing terminal or through the configuration software;

[0106] Determining the accuracy range using ADC resolution: Assuming the ADC resolution is... Then the accuracy At least satisfy Accuracy is typically measured by quantization error or resolution. Determine the required level of accuracy, such as the minimum resolvable value for voltage measurements. Check the analog-to-digital converter (ADC) specifications of the data acquisition and processing terminal to ensure its resolution meets or exceeds the required accuracy requirements. For example, if 1mV accuracy is required, the ADC resolution should be at least 1mV or lower. In the terminal configuration, select or set the appropriate ADC resolution and gain to ensure the accuracy of data acquisition.

[0107] In step 106, after the configuration is completed, a preliminary data acquisition test is performed to verify whether the actual acquisition frequency and accuracy meet the set requirements. If there is a deviation, the configuration parameters can be adjusted according to the test results until the requirements are met. In practical applications, the influence of factors such as signal amplification factor and noise level on the final accuracy also needs to be considered.

[0108] The data processing and anomaly analysis module extracts key features of insulation performance from the raw data stored in the central storage unit, and uses the local anomaly factor algorithm to analyze anomaly patterns in the key features in real time. The steps include:

[0109] Step 201: Obtain the data stream of each sensor through the constructed sensor wireless communication network:

[0110] In the formula, Indicates sensor In time Data flow; Indicates sensor No. Such measurement in time The reading; Indicates the dimension of observation;

[0111] Step 202: Integrate all sensor data streams to obtain the sensor monitoring dataset, i.e. ;in, This represents the total number of sensors, and ;

[0112] Step 203: Process the sensor monitoring dataset Preprocessing is performed to identify indicators related to insulation performance, and a preprocessed monitoring dataset is obtained. Remove invalid or outlier measurements; use a sliding window method to detect and eliminate outliers. ,make In time arrive The average value within is The standard deviation is Then the outlier is defined as Outliers were removed, the preprocessed monitoring dataset was compiled, and labeled as... ;

[0113] Step 204: Based on the preprocessed monitoring dataset The key features of insulation performance were extracted, resulting in the first key feature, the second key feature, and the third key feature:

[0114] The harmonic component characteristics were extracted by analyzing the current and voltage signals using Fourier transform and marked as the first key feature: In the formula, This represents the signal in the frequency domain. Represents the original signal; This represents the relationship between frequency and time, specifically rotation and oscillation in the complex plane. The imaginary unit is used to ensure the direction of rotation of vectors in the complex plane. Represents frequency variables. This determines the rotational speed, i.e., the frequency. Integral operations, the expression for... The integral element;

[0115] The temperature gradient feature is obtained by calculating the ratio of the temperature difference to the distance between two points, and is marked as the second key feature: In the formula, This represents the temperature values ​​at two points. Indicates the distance between two points;

[0116] By calculating the pulse amplitude and repetition rate of the discharge, partial discharge characteristics are obtained and marked as the third key feature: In the formula, Indicates the pulse amplitude; They represent time. The maximum and minimum voltage values; Indicates the discharge repetition rate; Indicates the number of discharges; Indicates a unit of time;

[0117] Step 205: Combine the first key feature, the second key feature, and the third key feature to obtain the feature set. ,in, Indicates the number of features extracted;

[0118] Step 206: Use local anomaly factors Real-time analysis of anomalous patterns in key features:

[0119] H1. Calculate the local density deviation of each sample feature relative to its neighbors, let... Any sample feature in ,That The value is defined as follows: In the formula, express The set of nearest neighbors; Indicates density;

[0120] H2. The calculated value of the local density deviation is used as the anomaly score.

[0121] H3. Set an anomaly threshold, compare the anomaly score with the anomaly threshold, and if the anomaly score is less than the anomaly threshold, determine the sample characteristics. The score is considered normal; if the abnormal score is not less than the abnormal threshold, then the sample characteristics are determined. These are potential anomalies and are marked as anomalous features;

[0122] In step H3, a deep analysis is performed on the features marked as abnormal to identify potential precursors to insulation failure. For example, if the temperature of multiple sensors rises abnormally and is accompanied by an increase in current fluctuations, it may be a problem caused by overheating of the insulation material. In this case, the maintenance team should be notified immediately, and the timestamp and relevant feature values ​​of this abnormal event should be recorded.

[0123] The equipment performance evaluation module filters and identifies insulation voltage transformers with faults or abnormal conditions. The steps for evaluating the performance of insulation voltage transformers with faults or abnormal conditions by calculating the comprehensive gain value include:

[0124] Step 301: Based on the abnormal feature values ​​output by the data processing and anomaly analysis module, identify and mark the insulation voltage transformers that have faults or abnormal states.

[0125] Step 302: For equipment with identified faults or abnormal states, extract its historical operating data and maintenance records from the database;

[0126] In step 302, this data includes, but is not limited to: historical operating data: voltage fluctuations, current changes, temperature curves, humidity records, gas concentration monitoring, etc., which reflect the operating status and environmental conditions of the equipment at different points in time; maintenance records: the time, content, replaced parts, and test results after each maintenance, etc., which are crucial for analyzing the causes and speed of equipment performance degradation;

[0127] Step 303: After obtaining historical operating data and maintenance records of the faulty or abnormal equipment, proceed to the performance analysis stage: evaluate the overall performance of the faulty or abnormal equipment using the comprehensive gain value. The formula for calculating the comprehensive gain is: In the formula, The overall gain value is a quantitative parameter that integrates multiple performance indicators and is used to reflect the overall performance of the equipment under different conditions. Indicates the stability gain value; Indicates the efficiency gain value; Indicates the reliability gain value; These represent the weights of the stability gain, efficiency gain, and reliability gain, respectively. The weighting depends on the importance of each performance indicator to the overall performance evaluation;

[0128] In step 303, the stability gain can be calculated based on the fluctuation of key equipment parameters, for example, using the reciprocal of the stability index of voltage or current (such as standard deviation or coefficient of variation). The better the stability, the higher the gain value: Stability gain value = 1 / standard deviation or coefficient of variation (voltage fluctuation); Efficiency gain reflects the efficiency of the equipment in the energy conversion or transmission process. For insulated voltage transformers, it may involve the accuracy and efficiency of voltage conversion: Efficiency gain value = actual conversion efficiency / theoretical maximum efficiency; Reliability gain is calculated based on the equipment's failure rate and maintenance cycle. The lower the failure rate and the longer the maintenance cycle, the higher the reliability gain. ;

[0129] Step 304: When classifying faulty or abnormal equipment by combining the comprehensive gain value, the comprehensive gain value of the faulty or abnormal equipment is matched with the preset gain threshold to obtain a normal maintenance label, an important maintenance label, or an emergency maintenance label.

[0130] Specifically, if the overall gain value is less than a preset gain threshold, the device in a faulty or abnormal state is judged to have extremely poor performance, a first maintenance signal is generated, and the corresponding faulty or abnormal state device is associated with an emergency maintenance tag; if the overall gain value is not less than a preset gain threshold and not greater than a preset gain threshold, ... , If the value is a real number greater than one hundred, the device is judged to have poor performance due to a fault or abnormal state, a second maintenance signal is generated, and the corresponding fault or abnormal state device is associated with an important maintenance tag; if the overall gain value is greater than a preset gain threshold... If the device is found to be in a faulty or abnormal state, its performance is deemed to be generally poor, a third maintenance signal is generated, and the corresponding faulty or abnormal state device is associated with a normal maintenance tag.

[0131] Step 305: Develop an equipment maintenance plan based on the results of the comprehensive gain value evaluation: For the first maintenance signal, the equipment performance is severely degraded and cannot be restored through maintenance, requiring upgrades or replacement to improve the reliability and efficiency of the entire system; for the second maintenance signal, the equipment has already failed and requires corrective maintenance to ensure the equipment returns to normal operation; for the third maintenance signal, the equipment performance has declined but has not yet failed, requiring preventive maintenance, such as replacing aging parts and adjusting operating parameters, to prevent failures from occurring.

[0132] In this embodiment of the invention, a smart sensor network is built to achieve multi-point, all-round real-time monitoring of insulation voltage transformers, improving monitoring coverage and accuracy. By configuring a high-efficiency data acquisition and processing terminal, sensor data can be collected quickly and accurately, and preliminary processing can be performed, reducing data transmission volume and improving system efficiency. By using wireless transmission of raw data to the central storage unit in real time, the complexity and limitations of wired connections are avoided, enhancing the system's flexibility and scalability. By extracting key features of insulation performance, strong support is provided for subsequent analysis. By utilizing an advanced local anomaly factor algorithm to analyze abnormal patterns in key features in real time, insulation performance degradation or potential faults can be detected promptly. By identifying insulation voltage transformers with faults or abnormal states, false alarms and missed alarms are reduced. By calculating the comprehensive gain value index, a detailed performance evaluation of problematic equipment is conducted, providing a scientific basis for subsequent maintenance, replacement, or optimization. In summary, this invention addresses the problems of incomplete insulation performance monitoring and insufficient evaluation of insulation voltage transformers. In practice, more data and contextual information may be needed to make specific decisions and optimization schemes.

[0133] Reference Figure 2 A method for evaluating the insulation performance of an insulated voltage transformer, comprising:

[0134] Define the monitoring parameters of the insulation voltage transformer, build an intelligent sensor network, and configure a data acquisition and processing terminal to collect data; transmit the collected raw data to the central storage unit in real time via wireless means.

[0135] Key features of insulation performance are extracted from the raw data stored in the central storage unit, and the local anomaly factor algorithm is used to analyze the anomaly patterns in the key features in real time.

[0136] The system filters and identifies insulated voltage transformers that are faulty or in abnormal condition, and evaluates their performance by calculating the comprehensive gain value.

[0137] Furthermore, the formulas mentioned above are all dimensionless calculations, derived from software simulation using a large amount of collected data to approximate the real situation. The proportionality coefficients in the formulas and the preset thresholds in the analysis process are set by those skilled in the art based on the actual situation or obtained through large-scale data simulation. The magnitude of the proportionality coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The magnitude of the proportionality coefficient depends on the amount of sample data and the processing coefficients initially set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantified value, it is acceptable.

[0138] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. The focus of each embodiment is on its differences from other embodiments. In particular, the apparatus embodiments are described simply because they are fundamentally based on the method embodiments; relevant details can be found in the descriptions of the method embodiments.

[0139] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0140] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0142] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0143] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0144] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0145] In conclusion, the above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An insulation performance evaluation system for an insulated voltage transformer, characterized by: The insulation monitoring and sensor optimization module, the central storage unit, the data processing and anomaly analysis module, and the device performance evaluation module are included. The insulation monitoring and sensor optimization module is used to determine the monitoring parameters of the insulation voltage transformer, build an intelligent sensor network, and configure a data acquisition and processing terminal to collect data. The data processing and anomaly analysis module is used to extract key features of insulation performance from the raw data stored in the central storage unit and analyze abnormal patterns in the key features in real time using the local anomaly factor algorithm. Through the constructed sensor wireless communication network, the data stream of each sensor is obtained: , where represents a sensor at time ; and represents a sensor a first measurement at time ; and represents an observation dimension Integrating all sensor data streams, obtaining a sensor monitoring dataset, i.e. ; wherein denotes the total number of sensors, and ; Sensor monitoring dataset Perform preprocessing to obtain the preprocessed monitoring dataset. Using the sliding window method to detect and remove outliers, for ,make In time arrive The average value within is The standard deviation is Then the outlier is defined as Outliers were removed, the preprocessed monitoring dataset was compiled, and labeled as... ; Based on the pre-processed monitoring data set , extract key features of insulation performance to obtain a first key feature, a second key feature, and a third key feature; combine the first key feature, the second key feature, and the third key feature to obtain a feature set wherein, denotes the number of extracted features; Using local anomaly factors Real-time analysis of anomaly patterns in key features: H1. Calculate the local density deviation of each sample feature relative to its neighbors, let... Any sample feature in ,That The value is defined as follows: In the formula, express The set of nearest neighbors; Indicates density; H2, according to the calculation result value of the local density deviation, it is used as an abnormal score; H3, set an abnormal threshold, compare the abnormal score with the abnormal threshold, if the abnormal score is less than the abnormal threshold, determine that the sample feature is a normal value; if the abnormal score is not less than the abnormal threshold, determine that the sample feature is potentially abnormal, and is marked as an abnormal feature; The device performance evaluation module is used to filter and identify insulation voltage transformers with faults or abnormal states and evaluate the performance of insulation voltage transformers with faults or abnormal states by calculating the comprehensive gain value.

2. The system for evaluating insulation performance of an insulation voltage transformer according to claim 1, characterized in that: The process of the insulation monitoring and sensor optimization module to determine the monitoring parameters of the insulation voltage transformer, build an intelligent sensor network, and configure a data acquisition and processing terminal to collect data includes: According to the type, specification, and operating environment of the insulation voltage transformer, the specific parameters of the monitoring target are determined, including voltage, current, temperature, humidity, and gas concentration. Select sensors according to the monitoring target and specific parameters. After selecting the sensors, use the minimum vertex cover method to optimize the sensor layout, i.e., the arrangement of monitoring points. Use the shortest path algorithm to design the communication of the sensor network. According to the determined monitoring points and sensor selection results, install sensors around the voltage transformer. Configure the data acquisition and processing terminal to collect data, set the data acquisition frequency and accuracy range.

3. The system for evaluating the insulation performance of an insulation voltage transformer according to claim 2, characterized in that: The process of using the minimum vertex cover method to optimize the sensor layout includes: The coverage of a sensor is defined as all monitoring points are considered as vertices in a graph ; where, denotes the set of all monitoring points, denotes the set of possible connecting edges; For each monitoring point , define an indicator variable ; when , it means to place a sensor at , when , it means not to place a sensor; where denotes the index; For each monitoring point there is at least one monitoring point covering i.e. there is such that: wherein, is an element identifier, used to indicate the coverage status, represents an index; wherein, represents is in the coverage of, represents not in the coverage; minimizing the total number of sensors required, i.e. ; wherein the problem of the layout of the sensors is translated into finding a set of vertices in the graph such that any vertex in the graph covers all vertices within a radius of the vertex, while the value of the function is minimized.

4. The system for evaluating insulation performance of an insulation voltage transformer of claim 2, wherein: The process of using the shortest path algorithm to design the communication of the sensor network includes: Modeling a sensor network as a weighted graph ; wherein a set of sensor nodes is denoted by a set of communication paths connecting the sensor nodes is denoted by each path is assigned a weight representing a communication delay or energy consumption source node and target node , from to , select a path with the minimum total weight; let the distance from source node to target node be 0, and the distance from source node to all other nodes be infinite The shortest path algorithm starts by setting the source node... Add to the deterministic set; subsequently, each time, select the node furthest from the undeterministic set. The most recent node is added to the deterministic set, and all its neighbors are updated to the source node. The distance is calculated, and this process is repeated until all nodes are added to a defined set, i.e., the shortest distance of all nodes is determined.

5. The system for evaluating insulation performance of an insulation voltage transformer of claim 2, wherein: The process of configuring the data acquisition and processing terminal to collect data, setting the data acquisition frequency and accuracy range includes: Determine the data acquisition frequency and accuracy range. Select and configure the data acquisition and processing terminal. Obtain the acquisition frequency value through the frequency acquisition formula: Acquisition frequency value = required data point number / test period. The resolution of the ADC is used to determine the accuracy range: assuming the resolution of the ADC is then the accuracy at least meets .

6. The system for evaluating insulation performance of an insulation voltage transformer of claim 1, wherein: Based on the preprocessed monitoring data set, extract the key features of insulation performance to obtain the first, second, and third key features. By Fourier analysis of the current and voltage signals, the harmonic component features are extracted and labeled as the first key features: ; wherein is the representation of the signal in the frequency domain; is the original signal; is the representation of the frequency and time relationship, i.e. rotation and oscillation on the complex plane, is the imaginary unit, used to ensure the direction of rotation of the vector on the complex plane, is the frequency variable, determines the rotation speed, i.e. the frequency level; is the integral element in the integral operation of ; The temperature gradient feature is obtained by calculating the ratio of the temperature difference between two points to the distance between the two points, and is marked as a second key feature: ; wherein, represents the temperature value of the two points, represents the distance between the two points; By calculating the pulse amplitude of the discharge and the discharge repetition rate, the partial discharge characteristics are obtained and marked as the third key features: ; in the formula, represents the pulse amplitude; respectively represents the maximum voltage value and the minimum voltage value at time ; represents the discharge repetition rate; represents the number of discharges; represents the unit time.

7. The system for evaluating insulation performance of an insulation voltage transformer according to claim 1, characterized in that: The process of the device performance evaluation module to filter and identify insulation voltage transformers with faults or abnormal states and evaluate the performance of insulation voltage transformers with faults or abnormal states by calculating the comprehensive gain value includes: Based on the abnormal feature values output by the data processing and anomaly analysis module, determine and identify insulation voltage transformers with faults or abnormal states. For the determined fault or abnormal state device, extract its historical operating data and maintenance records from the database. After obtaining the historical operating data and maintenance records of the fault or abnormal state device, enter the performance analysis stage: The overall performance of the equipment in a fault or abnormal state is evaluated by a comprehensive gain value, wherein a calculation formula of the comprehensive gain is: ; wherein, represents a comprehensive gain value, i.e., a quantitative parameter integrating multiple performance indexes, and is used to reflect the overall performance of the equipment under different conditions; represents a stability gain value; represents an efficiency gain value; represents a reliability gain value; respectively represent weights of the stability gain value, the efficiency gain value, and the reliability gain value, and ; In the classification of the fault or abnormal state equipment according to the comprehensive gain value, the comprehensive gain value of the fault or abnormal state equipment is matched with a preset gain threshold value to obtain an ordinary maintenance label, an important maintenance label or an emergency maintenance label; If the comprehensive gain value is less than the preset gain threshold, it is determined that the performance of the fault or abnormal state equipment is extremely poor, a first maintenance signal is generated, and the corresponding fault or abnormal state equipment is associated with an emergency maintenance label. , If the comprehensive gain value is greater than the preset gain threshold, it is determined that the performance of the fault or abnormal state equipment is general, a third maintenance signal is generated, and the corresponding fault or abnormal state equipment is associated with a normal maintenance label. If the comprehensive gain value is greater than the preset gain threshold, it is determined that the performance of the fault or abnormal state equipment is general, a third maintenance signal is generated, and the corresponding fault or abnormal state equipment is associated with a normal maintenance label. According to the evaluation result of the comprehensive gain value, a device maintenance plan is formulated: for the generated first maintenance signal, the device performance is seriously degraded and cannot be restored by maintenance, and needs to be upgraded or replaced; for the generated second maintenance signal, the device has failed and needs to be repaired; for the generated third maintenance signal, the device performance has decreased but has not yet appeared serious failure, and needs to be maintained preventively.

8. A method for evaluating the insulation performance of an insulation voltage transformer, characterized by, The method is applied to the insulation performance evaluation system of the insulation voltage transformer of any one of claims 1-7, and the method comprises: The monitoring parameters of the insulation voltage transformer are determined, an intelligent sensor network is built, and a data acquisition and processing terminal is configured to acquire data; the acquired original data is transmitted in real time to a central storage unit in a wireless manner; Key features of the insulation performance are extracted from the original data stored in the central storage unit, and the abnormal patterns in the key features are analyzed in real time by using a local outlier factor algorithm; The insulation voltage transformer in a fault or abnormal state is filtered and identified, and the performance of the insulation voltage transformer in the fault or abnormal state is evaluated by calculating a comprehensive gain value.

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